Self-learning statistical natural language processing for automatic production of virtual personal assistants

ABSTRACT

Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents. The computing device may support automated analysis and tuning to improve request processing. Other embodiments are described and claimed.

CROSS-REFERENCE TO RELATED U.S. PATENT APPLICATION

This application is a continuation application of U.S. application Ser.No. 15/332,084, entitled “SELF-LEARNING STATISTICAL NATURAL LANGUAGEPROCESSING FOR AUTOMATIC PRODUCTION OF VIRTUAL PERSONAL ASSISTANTS,”which was filed on Oct. 24, 2016, and which is a continuationapplication of U.S. application Ser. No. 14/341,013, entitled“SELF-LEARNING STATISTICAL NATURAL LANGUAGE PROCESSING FOR AUTOMATICPRODUCTION OF VIRTUAL PERSONAL ASSISTANTS,” which was filed on Jul. 25,2014, and which claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application Ser. No. 61/858,151, entitled “SYSTEM ANDMETHODS FOR SELF-LEARNING STATISTICAL NATURAL LANGUAGE QUERYUNDERSTANDING, GENERATION AND ENHANCEMENT FOR AUTOMATIC PRODUCTION OFVIRTUAL PERSONAL ASSISTANTS,” which was filed on Jul. 25, 2013.

BACKGROUND

As smart mobile devices become widespread and ubiquitous, naturallanguage interactions are becoming popular for daily functionalitiessuch as information retrieval, shopping assistance, reservations,ticketing, social-media postings, correspondence, note-taking andtime-management. Some devices may include a virtual personal assistant(VPA) to provide a natural language interface to those functionalities.Typically, VPAs require manual tuning and/or configuration to understandadditional natural language queries and/or provide additionalfunctionality.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified block diagram of at least one embodiment of asystem for self-learning natural language processing;

FIG. 2 is a simplified block diagram of at least one embodiment of anenvironment of a computing device of FIG. 1;

FIG. 3 is a simplified flow diagram of at least one embodiment of amethod for self-learning natural language processing that may beexecuted by the computing device of FIGS. 1 and 2;

FIG. 4 is a simplified flow diagram of at least one embodiment of amethod for generating a semantic model that may be executed by thecomputing device of FIGS. 1 and 2;

FIG. 5 is a simplified flow diagram of at least one embodiment of amethod for semi-supervised identification of contextual semanticfeatures that may be executed by the computing device of FIGS. 1 and 2;

FIG. 6 is a simplified flow diagram of at least one embodiment of amethod for unsupervised identification of contextual semantic featuresthat may be executed by the computing device of FIGS. 1 and 2;

FIG. 7 is a simplified flow diagram of at least one embodiment of amethod for ontological indexing that may be executed by the computingdevice of FIGS. 1 and 2;

FIG. 8 is a simplified flow diagram of at least one embodiment of amethod for processing a natural language request that may be executed bythe computing device of FIGS. 1 and 2;

FIG. 9 is a schematic diagram illustrating a candidate alternativelattice that may be generated by the computing device of FIGS. 1 and 2;

FIG. 10 is a simplified flow diagram of at least one embodiment of amethod for user dialog session management that may be executed by thecomputing device of FIGS. 1 and 2; and

FIG. 11 is a simplified flow diagram of at least one embodiment of amethod for analyzing and tuning natural language processing that may beexecuted by the computing device of FIGS. 1 and 2.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (B and C); (A and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (B and C); (A and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, an illustrative system 100 for self-learningnatural language processing includes a computing device 102 and, in someembodiments, a client device 104, which may be in communication witheach other over a network 106. In use, a user may input a naturallanguage request to the computing device 102. The user may input therequest directly to the computing device 102, for example using a textor a speech input device, or may input the request via a remote clientdevice 104, for example a smartphone. The computing device 102 generatesa lattice of potential interpretations of the request using alinguistic/phonetic speech model as well as a semantic model. Thecomputing device 102 may generate the semantic model offline, prior toreceiving the natural language request, based on a corpus of samplerequests. The computing device 102 weighs each candidate of the latticeusing a composite weight based on both the linguistic/phonetic model andthe semantic model. The computing device 102 generates the semanticrepresentation of the request by finding the optimal route through thelattice, based on the composite weighting of each alternative. Thesemantic representation may be provided to a dialog manager thatrecords, predicts, and generates dialog interactions such as responses,additional requests, and may perform requested user interactions. Thecomputing device 102 may automatically tune parameters of the semanticmodel, the lattice generation, and/or other operations of the computingdevice 102, based on the results of the natural language requestdecoding. Thus, the system 100 facilitates producing and/or tuning VPAsautomatically. The system 100 may facilitate the understanding,generation, and enhancement of natural language requests, usingself-learning statistical methods and machine learning algorithms. Theresulting VPAs may be highly robust to noisy input, which may resultfrom foreign accents or acoustically noisy environments in spoken input,malformed textual input, or highly-specified or esoteric domains ofknowledge.

The computing device 102 may be embodied as any type of device capableof performing the functions described herein. For example, the computingdevice 102 may be embodied as, without limitation, a smartphone, acellular phone, a tablet computer, a notebook computer, a laptopcomputer, a desktop computer, a workstation, a server computing device,a distributed computing system, a multiprocessor system, a consumerelectronic device, a smart appliance, and/or any other computing devicecapable of processing natural language requests. As shown in FIG. 1, theillustrative computing device 102 includes a processor 120, an I/Osubsystem 122, memory 124, and a data storage device 126. Of course, thecomputing device 102 may include other or additional components, such asthose commonly found in a portable computer (e.g., various input/outputdevices), in other embodiments. Additionally, in some embodiments, oneor more of the illustrative components may be incorporated in, orotherwise from a portion of, another component. For example, the memory124, or portions thereof, may be incorporated in the processor 120 insome embodiments.

The processor 120 may be embodied as any type of processor capable ofperforming the functions described herein. For example, the processormay be embodied as a single or multi-core processor(s), digital signalprocessor, microcontroller, or other processor or processing/controllingcircuit. Similarly, the memory 124 may be embodied as any type ofvolatile or non-volatile memory or data storage capable of performingthe functions described herein. In operation, the memory 124 may storevarious data and software used during operation of the computing device102 such as operating systems, applications, programs, libraries, anddrivers. The memory 124 is communicatively coupled to the processor 120via the I/O subsystem 122, which may be embodied as circuitry and/orcomponents to facilitate input/output operations with the processor 120,the memory 124, and other components of the computing device 102. Forexample, the I/O subsystem 122 may be embodied as, or otherwise include,memory controller hubs, input/output control hubs, firmware devices,communication links (i.e., point-to-point links, bus links, wires,cables, light guides, printed circuit board traces, etc.) and/or othercomponents and subsystems to facilitate the input/output operations. Insome embodiments, the I/O subsystem 122 may form a portion of asystem-on-a-chip (SoC) and be incorporated, along with the processor120, the memory 124, and other components of the computing device 102,on a single integrated circuit chip.

The data storage device 126 may be embodied as any type of device ordevices configured for short-term or long-term storage of data such as,for example, memory devices and circuits, memory cards, hard diskdrives, solid-state drives, or other data storage devices. The datastorage device 126 may store input natural language requests, samplerequests, statistical information, or other data used for naturallanguage processing.

The computing device 102 further includes communication circuitry 128,which may be embodied as any communication circuit, device, orcollection thereof, capable of enabling communications between thecomputing device 102, the client device 104, and/or other remotedevices. The communication circuitry 128 may be configured to use anyone or more communication technology (e.g., wireless or wiredcommunications) and associated protocols (e.g., Ethernet, Bluetooth®,Wi-Fi®, WiMAX_(n) etc.) to effect such communication.

In some embodiments, the computing device 102 may also include one ormore peripheral devices 130. The peripheral devices 130 may include anynumber of additional input/output devices, interface devices, and/orother peripheral devices. For example, the peripheral devices 130 mayinclude typical input/output devices such as a display, keyboard, and/ortouchscreen, location circuitry such as GPS receivers, or otherperipheral devices.

In those embodiments in which the system 100 includes the client device104, the client device 104 is configured to submit natural languagerequests to the computing device 102. The client device 104 may beembodied as any type any type of device capable of performing thefunctions described herein, such as, without limitation, a smartphone, acellular phone, a tablet computer, a notebook computer, a laptopcomputer, a desktop computer, a consumer electronic device, anin-vehicle infotainment system, a wearable computing device, a smartappliance, and/or any other computing device capable of submittingnatural language requests. Illustratively, the client device 104includes a processor 140, an I/O subsystem 142, memory 144, a datastorage device 146, communication circuitry 148, peripheral devices 150,and/or other components and devices commonly found in a smartphone orsimilar computing device. The individual components of the client device104 may be similar to the corresponding components of the computingdevice 102, the description of which is applicable to the correspondingcomponents of the client device 104 and is not repeated herein so as notto obscure the present disclosure. Additionally, FIG. 1 illustrates asingle client device 104, it should be appreciated that in someembodiments multiple client devices 104 may access the computing device102 through the network 106.

As discussed in more detail below, the computing device 102 and theclient device 104 may be configured to transmit and receive data witheach other and/or other devices of the system 100 over the network 106.The network 106 may be embodied as any number of various wired and/orwireless networks. For example, the network 106 may be embodied as, orotherwise include, a wired or wireless local area network (LAN), a wiredor wireless wide area network (WAN), and/or a publicly-accessible,global network such as the Internet. As such, the network 106 mayinclude any number of additional devices, such as additional computers,routers, and switches, to facilitate communications among the devices ofthe system 100.

Referring now to FIG. 2, in the illustrative embodiment, the computingdevice 102 establishes an environment 200 during operation. Theillustrative environment 200 includes a semantic compiler module 202,request decoder module 204, dialog management module 208, and tuningmodule 210. The various modules of the environment 200 may be embodiedas hardware, firmware, software, or a combination thereof.

The semantic compiler module 202 is configured to analyze a samplerequest corpus 212 and generate a semantic model 216 as a function ofthe sample request corpus 212. The sample request corpus 212 may beembodied as any predefined collection of user requests and/or otherrecorded user interactions. For example, the sample request corpus 212may include a large web corpus of sample web pages or otherinteractions. Additionally, or alternatively, the sample request corpus212 may include a highly-specified database, ontology, or catalogscheme. In some embodiments, sample request corpus 212 may includeuser-specified information, such as personal messages, calendars,contacts, or other information. The semantic model 216 may includemappings between natural language requests 214 or certain forms ofnatural language requests 214 and semantic representations 220 of thenatural language requests 214. The semantic representations 220 mayidentify user intents (such as particular intended actions orinteractions) and slots associated with the user intents. The slots mayinclude parameters, fields, options, or other data associated with aparticular user intent. The semantic compiler module 202 may generatethe semantic model 216 in an offline manner; that is, prior to thecomputing device 102 processing any live natural language requests 214from users.

The request decoder module 204 is configured to decode a naturallanguage request 214 using the semantic model 216, and generate asemantic representation 220 corresponding to the natural languagerequest 214. The request decoder module 204 statistically identifies theintent of a given natural language request 214 and extracts the relevantslots from the natural language request 214. The request decoder module204 may generate a candidate alternatives lattice 218 indicative of thenatural language request 214, assign a composite confidence weight toeach candidate alternative of the lattice 218, determine an optimalroute through the lattice 218 based on the weighting, and generate thesemantic representation 220 based on the optimal route through thelattice 218. Each candidate alternative of the lattice 218 may representan alternative representation of a token, word, or other fragment of thenatural language request 214. The request decoder module 204 may employa highly productive generation function to generate a large diversity ofcandidate alternatives based on similarity, pattern matching, or otherstatistical operations using the semantic model 216, as well as based onphonetic similarity, linguistic fluency, or other operations using alanguage model. In some embodiments, the request decoder module 204 mayconvert a spoken natural language request 214 into a textualrepresentation. The request decoder module 204 may generate a lattice ofpotential textual representations of the natural language request 214.In some embodiments, those functions may be performed by a sub-module,such as a speech recognition module 206.

The dialog management module 208 is configured to process the semanticrepresentation 220 to perform a user dialog session. Each user dialogsession may include one or more user requests, responses, actions orintents performed, or other user dialog interactions. In someembodiments, the dialog management module 208 may maintain recorded userdialog sessions 222, which may store information on past user dialogsessions. The dialog management module 208 may determine whether thesemantic representation 220 includes sufficient information to performthe natural language request 214. If sufficient information exists, thedialog management module 208 may perform the request (e.g., perform arequested user intent, generate a requested response, etc.). Ifsufficient information does not exist, the dialog management module 208may generate a response including a query for additional information.The dialog management module 208 may generate natural language responsesusing the semantic model 216, for example, modifying the response to bein interrogative or imperative form. The dialog management module 208may identify additional user intents that are likely to follow theintent of the current semantic representation 220, based on the recordeduser dialog sessions 222. A method for user dialog session management isdescribed further below in connection with FIG. 10.

The tuning module 210 is configured to update the semantic model 216and/or other parameters of the semantic compiler module 202, the requestdecoder module 204, and/or the dialog management module 208 in responseto processing natural language requests 214. The tuning module 210 mayautomatically refine semantic model 216 mappings, confidence weightings,candidate alternative generation thresholds, dialog trajectories, orother configuration parameters of the computing device 102. In someembodiments, the tuning module 210 may update the semantic model 216and/or other parameters based on additional sample data provided by auser such as a system administrator. In some embodiments, the tuningmodule 210 may allow a system administrator to perform interactiveanalysis and/or tuning of the computing device 102. A method analyzingand tuning natural language processing is described further below inconnection with FIG. 11.

Referring now to FIG. 3, in use, the computing device 102 may execute amethod 300 for natural language request processing. The method 300begins with block 302, in which the computing device 102 generates thesemantic model 216 by analyzing the sample request corpus 212. Thecomputing device 102 may analyze the sample request corpus 212 in anoffline manner; that is, the computing device 102 may analyze the samplerequest corpus 212 prior to processing any natural language requests214. The computing device 102 may analyze the sample request corpus 212using any statistical or machine learning algorithm useable to preparethe semantic model 216 for natural language processing. As describedabove, the semantic model 216 includes mappings between forms of naturallanguage requests 214 onto various user intents and associated slots.For example, the semantic model 216 may map a natural language request214 such as “I'd like to order 2 tickets to see Movie Title thisevening” onto a “ticket-reservation” user intent with a “ticketquantity” slot equal to 2, a “move name” slot equal to “Movie Title,”and a “time” slot equal to “this evening.” Similarly, a natural languagerequest 214 such as “Get me 2 seats for Movie Title at 7 p.m.” may maponto the same “ticket-reservation” user intent, with the “ticketquantity” slot equal to 2, the “movie name” slot equal to “Movie Title,”and the “time” slot equal to “7 p.m.” Methods for generating thesemantic model 216 are described further below in connection with FIGS.4-7.

After some time, the method 300 proceeds to block 304, in which thecomputing device 102 generates semantic representations 220corresponding to natural language requests 214, using the semantic model216 generated previously. The semantic representations 220 generatedeach identify a user intent corresponding to the natural languagerequest 214, and may also identify a number of slots corresponding toparameters or other data relevant to the user intent. The computingdevice 102 generates the semantic representation 220 by generating alattice 218 of candidate alternative interpretations of the naturallanguage request 214. Each candidate alternative may be generated basedon any combination of semantic, lexical, or phonetic analysis of thenatural language request 214 and/or of other candidate alternatives ofthe lattice 218. A method for generating the candidate alternativelattice 218 and generating the semantic representation 220 is describedfurther below in connection with FIG. 8.

In block 306, the computing device 102 engages in a user dialog sessionsbased on the semantic representations 220 of the natural languagerequests 214. The particular action and/or response taken by thecomputing device 102 may depend on the contents of the semanticrepresentations 220 and/or on the contents of the recorded user dialogsessions 222. For example, if a semantic representation 220 fullydescribes a user intent and all of its mandatory slots, the computingdevice 102 may perform an action associated with the user intent (e.g.,schedule a meeting). If the semantic representation 220 does not fullydescribe the user intent, the computing device 102 may produce a list ofpossible responses that is limited by some relevancy factor (e.g.,distance, price, etc.). As another example, if the semanticrepresentation 220 does not fully describe the user intent, thecomputing device 102 may generate a request for additional information.In some embodiments, the computing device 102 may determine a likelyadditional user intent based on the recorded user dialog sessions 222and generate a response that suggests that additional user intent. Amethod for user dialog session management is described further below inconnection with FIG. 10.

In block 308, the computing device 102 automatically tunes systemparameters based on the decoding of the natural language request 214and/or on the recorded user dialog sessions 222. The computing device102 may, for example, adjust scores, probability weights, thresholds, orother parameters used when generating the semantic model 216, thecandidate alternative lattice 218, and/or the semantic representation220. The computing device 102 may also alter the trajectory of userdialogs based on the recorded user dialog sessions 222. After tuning thesystem, the method 300 may loop back to block 302 to re-generate thesemantic model 216.

Although illustrated as proceeding sequentially, it should be understoodthat the processes described in FIG. 3 may be performed at differenttimes or in other orders, for example in parallel or asynchronously. Forexample, the semantic model 216 may be generated and/or updated by anoffline process independent of processing the natural language requests214. Additionally, user dialog sessions may iteratively decode and acton natural language requests 214 without tuning the system orregenerating the semantic model 216. Similarly, the system may be tunedby a process independent of processing natural language requests 214and/or generating the semantic model 216.

Referring now to FIG. 4, in use, the computing device 102 may execute amethod 400 for generating the semantic model 216. The method 400 may beexecuted, for example, as part of block 302 as described above inconnection with FIG. 3. The method 400 begins with block 402, in whichthe computing device 102 generates the semantic model 216 by identifyingone or more contextual semantic features (CSFs) in the sample requestcorpus 212. The CSFs may include one or more contextual semantic intentfeatures (CSIFs). Each CSIF may be defined as a sequence of lexical orphrasal sets that identifies a specific intent along with its slots.CSIFs may not be limited to closed lexical sets, and may be definedusing open-ended lexical sets as well as semantically synonymousphrases. Open lexical sets may be generated using general entity andrelation extractors as well as content databases such as catalogues, andsynonymous phrases may be generated using rephrasing data sources suchas thesauri. For example, a CSIF may be defined as (order-verb,ticket-object, for-relation, <Movie>, Time). That CSIF may be associatedwith the “ticket-reservation” user intent, and may match naturallanguage requests 214 such as “I'd like to order 2 tickets to see MovieTitle this evening.” The identified CSFs may also include one or morecontextual semantic slot features (CSSFs). Each CSSF may be defined as asequence of lexical or phrasal sets identifying a specific slot-familywithout regard to intent.

In some embodiments, in block 404 the computing device 102 may apply asemi-supervised algorithm to identify CSFs. The semi-supervisedalgorithm may process entries in the sample request corpus 212 or otherdata that has been manually tagged to identify CSFs. One method forapplying a semi-supervised algorithm is described below in connectionwith FIG. 5. Additionally or alternatively, in some embodiments in block406, the computing device 102 may apply an unsupervised algorithm toidentify CSFs. The unsupervised algorithm may identify CSFs withoutrequiring manual identification of CSFs in the sample request corpus 212or other data. One method for applying an unsupervised algorithm isdescribed below in connection with FIG. 6.

In block 408, the computing device 102 generates the semantic model 216by indexing one or more ontology. An ontology may be embodied as aformal representation of a specific domain or family of domains given bya graph, or an alternate data structure, comprising objects, properties,entities, relations between objects, relations between entities, andrelations between relations (higher-order relations) as well astaxonomies of objects. Thus, an ontology may reflect the sum ofknowledge embodied in a domain or family of domains. The computingdevice 102 may index the ontology such that the ontology may be searchedand matched against a natural language request 214. At least oneembodiment of a method for ontology indexing is described further belowin connection with FIG. 7.

After generating the semantic model 216 in block 408, the method 400 iscompleted, and the semantic model 216 is complete and may be used toprocess natural language requests 214. Although illustrated as bothidentifying CSFs and performing ontology indexing, it should beunderstood that in some embodiments the computing device 102 may performone or both of those operations, and in any order. In addition toontology indices and CSFs, the computing device 102 may also learngeneric terms that may serve as fillers inconsequential to queryunderstanding, e.g., terms such as “please,” “I'd like to,” “can I,” andother similar phrases. Such generic terms may be used to both hone theCSFs and weighting schemes employed by ontology indices, as well asgenerate a large corpus of natural queries based on seed or samplequeries for use by all modules of the computing device 102.

Referring now to FIG. 5, in use, the computing device 102 may execute amethod 500 for applying a semi-supervised algorithm to generate thesemantic model 216. The method 500 may be executed, for example, as partof block 404 as described above in connection with FIG. 4. The method500 begins with block 502, in which the computing device 102 seeds thesemantic model 216 based on predefined requests within the samplerequest corpus 212. In some embodiments, in block 504 the computingdevice 102 may extend the initialization using recorded user data. Forexample, the computing device 102 may also process user-generated dataand history including contacts, meetings, calendar, text messages,emails, and documents. In some embodiments, the computing device 102 mayprocess the recorded user dialog sessions 222, which may be used tolearn the relative weight of slots for each user intent. In block 506,the computing device 102 tags the sample requests to identify associateduser intents and slots. The tag information may be provided by a user orother external data source. Thus, tagging process may be a supervised orsemi-supervised process.

In block 508, the computing device 102 extracts CSFs from the taggedsample requests. As described above, for each CSF the computing device102 may identify a sequence of lexical or phrasal sets. The computingdevice 102 may associate a user intent with the CSF, and may associateone or more slots with the lexical or phrasal sets of the CSF. Forexample, given a sample request, “I want to meet John tomorrow about thelatest numbers,” the computing device 102 may extract a CSF=(meet-verb,<Person>, <Time>, about <Topic>). As another example, given a samplerequest, “Please set an appointment with Mary at 5 p.m. to discuss thelatest numbers,” the computing device 102 may extract a CSF=(appointmentwith, <Person>, at <Time>, to discuss <Topic>). Both of those extractedCSFs may be associated with an “appointment” or “meeting” user intent,as well as a number of slots for person, time, or topic.

In some embodiments, in block 510, the computing device 102 may performan initial evaluation of statistical significance for each CSF. Thecomputing device 102 may perform any statistical operation to determinethe relative level to which the CSF serves as an indication for theassociated user intent and/or slot. For example, the computing device102 may calculate the frequency of each CSF per user intent.Additionally or alternatively, the computing device 102 may calculate anormalized frequency measuring the statistical significance of a givenCSF an indication of a specific user intent and/or slot relative to thesample request corpus 212. Similarly, for each user intent, slots may beweighted differently according to occurrence frequency, producing adistinction between mandatory, preferred, and optional slots for theassociated user intent.

In block 512, after analyzing the tagged sample requests, the computingdevice 102 analyzes untagged recorded user data using the CSFs alreadyidentified in the semantic model 216. The untagged user data may beembodied as any natural language input (e.g., speech or text input)provided by the user. As the user data is untagged, the user is notrequired to further identify intents and/or slots. The computing device102 may extract additional CSFs from the untagged recorded user databased on similarity to existing CSFs. Continuing the previous exampledescribed above, given untagged user data, “I want to meet John tomorrowto discuss the latest numbers,” the computing device 102 may extract aCSF=(meet-verb, <Person>, <Time>, to discuss <Topic>). In someembodiments, in block 514 the computing device 102 may updateprobabilities associated with each CSF in the semantic model 216,including previously identified CSFs, for example CSFs extracted fromthe tagged sample requests. For example, the computing device 102 mayupdate the frequencies of each CSF per user intent and/or update thenormalized frequency for each CSF.

In block 516, the computing device 102 analyzes a larger, untaggedcorpus of requests using the CSFs already identified in the semanticmodel 216. For example, the computing device 102 may analyze the fullsample request corpus 212, a large web corpus, a user-defined ontology,a user database, or other relatively large data source. In someembodiments, in block 518 the computing device 102 may update theprobabilities associated with each CSF in the semantic model 216,including previously identified CSFs. Analyzing the larger corpus mayallow the computing device 102 to refine CSF contents and probabilities,to improve accuracy. For example, in the example described above, givenuntagged user data, “I want to meet John at about 7 a.m.,” the computingdevice 102 may extract an erroneous CSF=(meet-verb, <Person>, <Time>, atabout <Topic>). Analysis of the larger corpus, such as a web corpus, mayreveal that “at about” signifies a time relationship. Thus, afteranalyzing the larger corpus, that CSF may be modified or replaced withCSF=(meet-verb, <Person>, <Time>, at about <Time>).

As shown in FIG. 5, after completing the analysis of the larger corpus,the method 500 is complete. In some embodiments, rather than completethe method 500, the computing device 102 may continue to extract CSFsfrom larger corpora. For example, in some embodiments the method 500 mayrepeat the operations of block 516 for different corpora.

Referring now to FIG. 6, in use, the computing device 102 may execute amethod 600 for applying an unsupervised algorithm to generate thesemantic model 216. The method 600 may be executed, for example, as partof block 406 as described above in connection with FIG. 4. The method600 begins with block 602, in which the computing device 102 identifiesnamed entities and relationships in the predefined sample request corpus212. The computing device 102 may apply a number of named entity andrelation extractors to analyze the sample request corpus 212. Thecomputing device 102 may identify the named entities and relationshipswithout requiring manual tagging of the sample request corpus 212.

In block 604, the computing device 102 clusters the sample requestsaccording to entity and relation patterns using one or more unsupervisedclustering algorithms. For example, the computing device 102 may employk-means clustering or any other unsupervised clustering algorithm Inblock 606, the computing device 102 assigns a user intent and one ormore slots to each cluster of sample requests. For example, a number ofsample requests sharing a “meet” verb as well as <Person>, <Time>, and<Topic> entities may be clustered and assigned to a specific userintent. After assigning user intent and slots, the method 600 iscompleted.

Referring now to FIG. 7, in use, the computing device 102 may execute amethod 700 for indexing one or more ontologies to generate the semanticmodel 216. The method 700 may be executed, for example, as part of block408 as described above in connection with FIG. 4. The method 700 beginswith block 702, in which the computing device 102 loads an ontology foreach user intent. As described above, each ontology may be embodied as aformal representation of a specific domain or family of domains given bya graph, or an alternate data structure, comprising objects, properties,entities, relations between objects, relations between entities, andrelations between relations (higher-order relations) as well astaxonomies of objects. In some embodiments, in block 704, the computingdevice 102 may load a predefined ontology. The predefined ontology maybe a specialized ontology supplied by a user, a general ontologysupplied by a third party, or any other predefined ontology. In someembodiments, in block 706 the computing device 102 may produce anontology using CSF matching. Thus, additional ontologies may begenerated by the computing device 102 on an ongoing basis. For example,an ontology representing “movie theaters” may be constructed byanalyzing CSFs related to the domain “movie ticket ordering.”

In block 708, the computing device 102 indexes the ontologies for eachuser intent. The computing device 102 may index the ontology in anymanner that may be later searched and matched against natural languagerequests 214. For example, the ontologies may be indexed into one ormore vector spaces that may be searched by finding a nearest neighbormatch. In some embodiments, in block 710 the computing device 102 maymap an ontology onto one or more vector spaces. The computing device 102may assign a vector space V^(d) to each object-type of the ontology(i.e. entities, relations, properties, etc.). Each coordinate 1, . . . ,d may encode a token in the lexical string representing the relevantobject, property, or relation. As further described below, a naturallanguage request 214 may be evaluated by mapping the natural languagerequest 214 onto the same vector space and finding the ontology with theclosest vectors in each vector space. In block 712, the computing device102 may map slots associated with the user intent of the ontology to thevector space. Thus, after determining the closest-matching vectors, thecomputing device 102 may identify the user intent and slots associatedwith the natural language request 214.

Referring now to FIG. 8, in use, the computing device 102 may execute amethod 800 for processing a natural language request 214. The method 800may be executed, for example, as part of block 304 as described above inconnection with FIG. 3. The method 800 begins with block 802, in whichthe computing device 102 receives a natural language request 214. Thecomputing device 102 may receive the natural language request 214directly from a user, for example using a peripheral device such as akeyboard, touch screen, or microphone. Additionally or alternatively,the computing device 102 may receive the natural language request 214from a remote device such as a client device 104 via the network 106. Insome embodiments, in block 804 the computing device 102 may receivespeech input. The speech input may be embodied as raw audio data,compressed audio data, or any other representation of speech uttered bythe user. In some embodiments, in block 806 the computing device 102 mayreceive text input, for example entered by the user via a keyboard ortouchscreen.

In some embodiments, in block 808, the computing device 102 may convertthe natural language request 214 from speech format to text format.Rather than a single text interpretation of the speech input, thecomputing device 102 may generate a lattice of potential textinterpretations of the speech input. The computing device 102 may useany speech recognition engine or other technique for speech recognitionincluding, for example, weighted finite state transducers or neuralnetworks. The speech recognition engine may incorporate large beamwidth; that is, alternatives may not be pruned aggressively, so theresultant lattice of potential candidates may include many possiblealternatives. The speech recognition engine may include promiscuousphonetic modeling, such as a lexicon represented by relaxed phoneticmappings that account for foreign accent variations, ambiguouspronunciations, and noisy environments, and other phonetic alternativesto further expand the set of potential alternatives. The speechrecognition engine may use both a generic large web-based language model(LM) and/or domain-specific LMs constructed to optimally represent aspecific domain of knowledge. Knowledge domains may includeuser-generated data such as personal data, contacts, meetings, textmessages, emails, social postings, or other documents. Generic anddomain-specific LMs may be combined into a domain-biased genericweb-based LM by taking the union of sentences containing words orstemmed words that are either manually or automatically selected asrepresenting functional or content words of a given domain. In someembodiments, the speech recognition engine may create a class-basedgeneral LM, for example by using class tags and collapsing most frequentinstances of a class to a class tag, replacing class members in thecorpus by class tags, or by normalizing class members to a singlerepresentative. The classes may represent lists of entities that aredynamically gathered and originate from user-specific data such as namesof contacts, emails, or similar user data. Generic and domain-specificLMs may be combined using a machine learned weighting scheme and trainedsmoothing language models (e.g., the Kneser-Ney algorithm) to optimizelattice scores for both domain-specific and general queries. Asdescribed further below in connection with FIG. 11, parameters of thespeech recognition engine may be tuned automatically based on resulthistory.

In block 810, the computing device 102 generates the candidatealternative lattice 218 that includes multiple potential alternativerepresentations of the natural language request 214. The computingdevice 102 uses several different data sources and techniques togenerate the candidate alternatives. In block 812, the computing device102 generates candidate alternatives using the semantic model 216. Thecomputing device 102 may generate alternatives based on potentialmappings between candidate alternatives and user intents or slots. Forexample, the computing device 102 may generate candidate alternativesbased on the sequence of lexical sets of a CSF included in the semanticmodel 216, or based on an ontology indexed in the semantic model 216.

As described above, an ontology X_(n) may be indexed in the semanticmodel 216 by assigning a vector space V^(d) to each object type of theontology X_(n). To identify candidate alternatives, the natural languagerequest 214 may be deciphered by representing the natural languagerequest 214 as a vector of tokens according to the same scheme used forindexing the ontology X_(n). The computing device 102 may find theontology X_(n) with closest vectors in each vector space, V_(n,1)^(d(n,1)), . . . , V_(n,m) ^(d(n,m)). Since ontologies represent userintents, and the indexed vector spaces represent respective slots,finding a closest match, or set of nearest neighbors, returns the intentand slots most accurately represented by the natural language request214. Note that closest match may be defined using various metrics andmeasurements, one example being cosine similarity over a Euclideanspace. The exact metrics, measurements and weights assigned tosimilarity scores may be learned and optimized using machine learningalgorithms such as logistic regressions, support vector machines, orconditional random fields. In some embodiments, standard classificationmethods (for example, using support vector machines with termfrequency/inverse document frequency of tokens as features) may be usedto deduce the relevant user intent, and ontology indexing methods may beapplied for extraction of slots.

In block 814, the computing device 102 generates candidate alternativesusing a phonetic or language model. In other words, the computing device102 may generate candidate alternatives that sound similar to existingalternatives, are syntactically correct, or are linguistically fluentbased on existing candidate alternatives. For example, the computingdevice 102 may generate phonetically enriched candidates based on eithera general or domain-specific LM or user-history generated LM.Additionally or alternatively, the computing device 102 may perform anyother technique to enrich the candidate alternative lattice 218. Forexample, the computing device 102 may generate additional candidatealternatives using semantic enrichments including synonyms, paraphrases,stemming, and generalizations; contextual feature sequences includingn-grams, switch-grams, skip-grams, colocation frequencies, andnormalized frequencies; entity and relation based features; or genericfillers that do not affect intent and slots (e.g., “please,” “can I,”etc.). In block 816, the computing device 102 annotates the candidatealternative lattice 218 to associate candidate alternatives withparticular intents and/or slots using the semantic model 216. Forexample, the computing device 102 may assign intents and/or slots basedon the degree of matching with CSFs or ontology indexes of the semanticmodel 216.

In block 818, the computing device 102 assigns composite confidenceweights to each candidate alternative in the candidate alternativeslattice 218. Each composite confidence weight represents the likelihoodthat that candidate alternative correctly represents the naturallanguage request 214. The composite confidence weights are based onseveral measures, including phonetic, linguistic, and/or semanticconfidence. In block 820, the computing device 102 assigns aphonetic/language model confidence to each candidate alternative. Forexample, the computing device 102 may assign a phonetic similarityvalue, a general LM confidence value; a domain LM confidence value, or alocal and non-local syntactic confidence value. In block 822, thecomputing device 102 assigns a semantic model confidence to eachcandidate alternative. For example, the computing device 102 may assignan intent-and-slot based confidence value. That confidence value may bedetermined, for example, based on the probabilities of the associatedCSF in the semantic model 216, or on the degree of matching to anontological index in the semantic model 216, or on any other statisticaldata available from the semantic model 216. The weights may becalculated taking into consideration full or partial matching, synonymor homophone matching, content or non-content generation, and thestrength of the relevant contextual feature.

Referring now to FIG. 9, the schematic diagram 900 illustratesgeneration of the candidate alternatives lattice 218. The diagram 900illustrates a natural language request 214 that is received from theuser. As described above, the natural language request 214 may bereceived as text or speech data. After any necessary input processing,the natural language request 214 is represented as a set of candidatealternatives C₀ through C₅. In the illustrative example, the candidatealternatives C₀, C₅ represent the beginning and end of the naturallanguage request 214, respectively. The candidate alternatives C₁through C₄ represent the output of speech recognition performed on theinput natural language request 214. For example, the candidatealternatives (C₁, C₂, C₃, C₄) may be embodied as (“I”, “won a”,“humble”, “girl”).

As shown, the input natural language request 214 is enhanced to generatethe candidate alternatives lattice 218. As shown, additional candidatealternatives C₆ through C₁₃ have been added to the lattice 218.Additionally, each of the candidate alternatives (other than thebeginning and end) has been assigned a composite confidence weightw_(i). Further, the candidate alternatives C₁₀ and C₁₁ have both beenassociated with a slot S₀ of the semantic model 216. Thus, the relativeweights of those candidate alternatives may affect what interpretationof the slot value is used. To illustrate the lattice enhancement, Table1, below, lists potential natural language tokens that may be associatedwith each candidate alternative. As shown, in the illustrative candidatealternatives lattice 218, the slot S₀ may be represented by thecandidate alternatives C₁₀ (“hamburger”) or C₁₁ (“ham and”). Forexample, the slot S₀ may be a “dish” slot associated with afood-ordering user intent.

TABLE 1 Example text for candidate alternatives lattice CandidateAlternative Token C₀ <start> C₁ I C₂ won a C₃ humble C₄ girl C₅ <stop>C₆ <silence> C₇ want a C₈ wanna C₉ humble C₁₀ hamburger C₁₁ ham and C₁₂girl C₁₃ gill

Referring back to FIG. 8, after building and enhancing the candidatealternatives lattice 218, in block 824 the computing device 102determines an optimal route through the lattice 218. The computingdevice 102 may determine the optimal route by finding the path ofcandidate alternatives having the greatest combined confidence weight.The computing device 102 may use any appropriate pathfinding oroptimization algorithm to find the optimal route. For example, thecomputing device 102 may, for each candidate alternative of the lattice218, calculate a normalized score which is not sensitive to the intentand slots and/or calculate local scores assigned to each intent and slotwhen the candidate alternative is generated. The computing device 102may also calculate a query and intent coverage score, that is, a scorereflecting the degree to which relevant slots are represented in thelattice 218 for a given natural language request 214 (for example, ifall mandatory slots for a given intent are covered by a candidate routeon the lattice 218, that route will receive a higher score than if notall mandatory slots are covered).

For example, referring again to FIG. 9, Table 2, below, includesillustrative composite confidence weight values w_(i) associated witheach candidate alternative C_(i). In that example, the path (C₀, C₁, C₇,C₁₀, C₅) corresponding to “I want a hamburger” may be the optimal routethrough the candidate alternatives lattice 218.

TABLE 1 Example text for candidate alternatives lattice Composite WeightValue w₁ 0.012 w₂ 0.004 w₃ 0.005 w₄ 0.006 w₅ N/A w₆ 0.009 w₇ 0.023 w₈0.008 w₉ 0.003 w₁₀ 0.019 w₁₁ 0.014 w₁₂ 0.006 w₁₃ 0.006

Referring back to FIG. 8, after determining the optimal route throughcandidate alternatives lattice 218, in block 826, in some embodiments,the computing device 102 may determine whether the confidence score ofthe optimal route exceeds a predefined threshold confidence score. Ifnot, the method 800 loops back to block 810 to generate additionalcandidate alternatives, re-weight the candidate alternatives, andre-find the optimal route. If the confidence score exceeds thethreshold, the method 800 proceeds to block 828.

In block 828, the computing device 102 generates a semanticrepresentation 220 corresponding to the optimal route through thecandidate alternatives lattice 218. As described above, the semanticrepresentation 220 includes a user intent and zero or more slots. Theslots may be mapped to particular tokens, text, or other valuesassociated with the one of the candidate alternatives lattice 218. Aftergenerating the semantic representation 220, the method 800 is completed,and processing of the natural language request 214 may continue. In someembodiments, some or all of the slots may not be mapped to any tokens orother values. As further described below, in those embodiments, thecomputing device 102 may prompt the user for values associated withun-mapped or missing slots. As an example of generating a semanticrepresentation 220, and referring again to FIG. 9, in the illustrativeembodiment, the optimal route through the candidate alternatives lattice218 may be the path (C₀, C₁, C₇, C₁₀, C₅) corresponding to “I want ahamburger.” In that example, the optimal path may be mapped to afood-ordering user intent, and the “dish” slot associated with thatintent, illustrated as slot S₀, may be mapped to “hamburger.”

Referring now to FIG. 10, in use, the computing device 102 may execute amethod 1000 for user dialog session management. The method 1000 may beexecuted, for example, as part of block 306 as described above inconnection with FIG. 3. The method 1000 begins with block 1002, in whichthe computing device 102 determines whether the semantic representation220 includes sufficient information to perform the associated userintent. In some embodiments, in block 1004, the computing device 102 maydetermine whether the semantic representation 220 includes sufficientinformation by determining whether all mandatory slots associated withthe user intent are included in the semantic representation 220. Thesemantic model 216 may include statistical information on the occurrenceof slots with each user intent. That statistical information may be usedto classify slots, for example, as optional, preferred, or mandatory. Inblock 1006, the computing device 102 determines whether sufficientinformation exists. If not, the method 1000 skips ahead to block 1010.If sufficient information is included, the method 1000 advances to block1008.

In block 1008, the computing device 102 performs the user intentassociated with the semantic representation 220, using information onslots included in the semantic representation 220. The computing device102 may use any technique to perform the user intent, including callingan internal or external application, module, plug-in, script interface,or other request completion module.

In block 1010, the computing device 102 generates a response to the userrequest based on the semantic representation 220 of the natural languagerequest 214. The computing device 102 may generate any appropriateresponse. For example, the computing device 102 may generate a responsethat provides information requested by the user, informs the user of theresults of performing the user intent, requests additional information,or suggests a follow-up user intent. In block 1012, in some embodiments,the computing device 102 may generate a list of adequate responses tothe natural language request 214 that are limited by a relevancylimitation. For example, the computing device 102 may return a list ofshopping results that are limited by geographical distance or by price.In block 1014, in some embodiments the computing device 102 may requestadditional information from the user. For example, if the semanticrepresentation 220 does not include one or more mandatory slots, or ifone or more slots have ambiguous decodings, the computing device 102 maygenerate a request for additional information concerning those slots. Inblock 1016, in some embodiments the computing device 102 may suggest oneor more additional user intents based on the recorded user dialogsessions 222. For example, based on historical data, the computingdevice 102 may determine that a particular user intent typically followsthe current user intent. For example, the computing device 102 maydetermine that a “reminder action” user intent typically follows a“meeting” user intent, and thus may suggest the “reminder action” inresponse to the current semantic representation 220 being associatedwith the “meeting” user intent.

In block 1018, the computing device 102 generates a natural languagerepresentation of the response. The computing device 102 may use anytechnique to prepare the natural language representation, includinggenerating a text representation and/or generating an audiorepresentation using a text-to-speech converter. In some embodiments, inblock 1020, the computing device 102 may use the semantic model 216 togenerate interrogative and/or imperative alternatives to the language ofthe response. For example, the computing device 102 may use the semanticmodel 216 to generate interrogative forms of the language used to querythe user for additional information on slots.

In block 1022, the computing device 102 records the natural languagerequest 214 and the associated natural language response in the recordeduser dialog sessions 222. The computing device 102 may store the requestand response in any appropriate format. In some embodiments, thecomputing device 102 may also store additional information associatedwith the request and/or response, such as the candidate alternativeslattice 218 or the semantic representation 220. As described above, therecorded user dialog sessions 222 may be used to learn typical userdialog interactions, and to predict likely dialog interactions.

In block 1024, the computing device 102 submits the natural languagerepresentation of the response to the user. The computing device 102may, for example, display the response on a display screen or output theresponse using a speaker or other audio device. In some embodiments, thecomputing device 102 may submit the response over the network 106 to aremote device, such as a client device 104. After submitting theresponse, the method 1000 is completed, and the computing device 102 maycontinue processing the natural language request 214 and/or processadditional natural language requests 214.

Referring now to FIG. 11, in use, the computing device 102 may execute amethod 1100 for analyzing and tuning the VPA components described above.The method 1100 may be executed, for example, as part of block 308 asdescribed above in connection with FIG. 3. The method 1100 begins withblock 1102, in which the computing device 102 updates the semantic model216 and associated confidence weights based on the results of decoding anatural language request 214. For example, the computing device 102 mayoptimize machine-learned (or otherwise determined) thresholds of thesemantic compiler module 202, optimize adaptable thresholds of therequest decoder module 204 (including both automatic speech recognitionparameters based on user-data and user-history, and recorded queries anddynamic domains, as well as candidate alternatives lattice 218generation thresholds including scores, weights and interpolationthereof), and optimize dialogue management module 208 trajectories. Inblock 1104, in some embodiments, the computing device 102 may identifyrequests that were decoded with complete confidence; that is, requestsin which the intent and slots were fully resolved with no ambiguities.Those requests may be classified as perfect extractions. Queries decodedwith complete confidence may be used for robust learning, as well asretrieval of equivalently-structured queries from across the web. Insome embodiments, in block 1106, the computing device 102 may identifyrequest fragments that were not decoded to a slot. The computing device102 may analyze those fragments as either generic functional wording(repeated across domains), or meaningful fragments that should be usedfor a new form of slot extraction. In block 1108, in some embodimentsthe computing device 102 may identify ambiguous slot decodings. Thecomputing device 102 may apply more fine-tuned subsequent resolutionstrategies to those ambiguous slot decodings.

In block 1110, the computing device 102 may update the semantic model216 and/or confidence weights based on user-supplied sample data. Forexample, a system administrator may submit sample queries, tagged data,and matching logical patterns. In block 1112, the computing device 102may perform interactive analysis of recorded requests. A systemadministrator may analyze the intent and slot resolution logic for eachrequest, filter all queries extracted according to a specific strategyand/or logical patterns, as well as view improvements and regressions(given tagged data) following changes in data or explicit logic and/orpattern changes. After performing that analysis, the method 1100 iscompleted. As described above, the computing device 102 may continue toprocess natural language requests 214 using the updated semantic model216.

Referring back to FIG. 3 as an illustrative example, the method 300 maybe used in a system 100 for food ordering. During execution of block302, in an offline stage, an ontology of restaurants may be constructed.The ontology may include of the following objects and relations:restaurant name, dishes on restaurant's menu, price of dish, descriptionof dish, and location of restaurant. The ontology may be indexed togenerate the semantic model 216. In the example, the sample requestcorpus 212 may include a large database of general user requests and/orqueries, some of which contain food-ordering requests. Usingunsupervised machine learning algorithms, requests may be clusteredaccording to slots matched by the ontology index. Additionally oralternatively, user queries for food ordering may be solicited. RelevantCSFs may be extracted from food ordering queries. Relevant weights maybe assigned to slots and functional elements of relevant CSFs. Forexample, a CSF such as (order-verb, quantity, dish-entity) may receive ascore reflecting the matching of the dish-entity to a dish in theontology index, or its respective description. Having compiled asemantic model 216, a validation-set of tagged queries may be employedto optimize scoring schemes using the CSFs and the ontology index. Oncethe semantic model 216 is consolidated, the computing device 102 mayexecute the block 304 online, employing an ASR engine optimized for thefood ordering domain, using a language model (LM) constructed fromin-domain user queries. Lattices may be generated using in-domain slots,and general LM fluency scores and phonetic similarity scores may bematched against the indexed ontology. For example, given a sentence suchas “I won a humble girl,” various candidates including “I want ahamburger” may be generated. The computing device 102 may select themaximally matching route identifying the mandatory slot “hamburger” asthe requested order. Since the decoded natural language request 214lacks the additional mandatory slot of the restaurant to be orderedfrom, during execution of block 306, the computing device 102 may engagein a user dialog session to request further information or alternativelypresenting all hamburgers that can be ordered within a predefinedradius. The method 300 may continue to decode subsequent naturallanguage requests 214 until an order is placed. The block 308 may beexecuted periodically, continuously, or responsively along the way toevaluate and improve the VPA components described above either entirelyautomatically or semi-automatically.

As another illustrative example, the method 300 may be used in a system100 for a virtual personalized shopping assistant. In this example, anontology may be created in block 302 by automatically scanningcatalogues and cross-matching products from various sources to allow forcatalogue-and-web-based paraphrasing. Such a shopping assistant mayemploy all modules of the system 100 and capitalizes on the variousmodules' products including domain specific language models, ontologyindexing, and appropriately tuned CSFs to automatically produce ashopping tool that is optimized for a user, vendor, and/or productdomain.

EXAMPLES

Illustrative examples of the devices, systems, and methods disclosedherein are provided below. An embodiment of the devices, systems, andmethods may include any one or more, and any combination of, theexamples described below.

Example 1 includes a computing device for interpreting natural languagerequests, the computing device comprising a semantic compiler module togenerate a semantic model as a function of a corpus of predefinedrequests, wherein the semantic model includes a plurality of mappingsbetween a natural language request and a semantic representation of thenatural language request, wherein the semantic representation identifiesa user intent and zero or more slots associated with the user intent;and a request decoder module to generate, using the semantic model, alattice of candidate alternatives indicative of a natural languagerequest, wherein each candidate alternative corresponds to a token ofthe natural language request; assign a composite confidence weight toeach candidate alternative as a function of the semantic model;determine an optimal route through the candidate alternative latticebased on the associated confidence weight; and generate a semanticrepresentation of the natural language request as a function of thecandidate alternatives of the optimal route.

Example 2 includes the subject matter of Example 1, and wherein togenerate the semantic model comprises to identify a contextual semanticfeature in the corpus, wherein the contextual semantic feature comprisesa sequence of lexical sets associated with a user intent and zero ormore slots associated with the user intent; determine a firstprobability of the contextual semantic feature given the user intent;and determine a normalized probability of the user intent as a functionof a rate of occurrence of the contextual semantic feature in thecorpus.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein to identify the contextual semantic feature comprises toidentify the contextual semantic feature using a semi-supervisedalgorithm

Example 4 includes the subject matter of any of Examples 1-3, andwherein to identify the contextual semantic feature using thesemi-supervised algorithm comprises to tag a first group of predefinedsample queries in the corpus to identify user intents and slots; extractthe contextual semantic feature from the sample queries; analyze, usingthe semantic model, a second group of predefined sample queries in thecorpus; extract additional contextual semantic features in response toanalyzing the second group of predefined sample queries; and update thefirst probability and the normalized probability in response toanalyzing the second group of predefined sample queries.

Example 5 includes the subject matter of any of Examples 1-4, andwherein the second group of predefined sample queries comprises recordeduser data or a web corpus.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to identify the contextual semantic feature comprises toidentify the contextual semantic feature using an unsupervisedalgorithm.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to identify the contextual semantic feature using theunsupervised algorithm comprises to identify predefined named entitiesand relationships in a first group of predefined sample queries in thecorpus; cluster the predefined sample queries using an unsupervisedclustering algorithm to generate a plurality of clusters; and assign auser intent and slots to each cluster of the plurality of clusters.

Example 8 includes the subject matter of any of Examples 1-7, andwherein to generate the semantic model comprises to generate anontological index as a function of a predefined ontology associated withthe user intent, wherein the ontology includes a plurality of objectsdescribing a knowledge domain.

Example 9 includes the subject matter of any of Examples 1-8, andwherein to generate the ontological index comprises to assign a vectorspace to an object type of the predefined ontology, wherein the vectorspace includes a plurality of coordinates, wherein each coordinateencodes a lexical token representing an associated object of theontology; and map a slot of the user intent associated with thepredefined ontology to the vector space.

Example 10 includes the subject matter of any of Examples 1-9, andwherein the request decoder module is further to receive arepresentation of speech data indicative of the natural languagerequest; and convert the representation of speech data to a firstlattice of candidate alternatives indicative of the natural languagerequest; wherein to generate the lattice of candidate alternativescomprises to generate the lattice of candidate alternatives in responseto conversion of the representation of speech data to the first latticeof candidate alternatives.

Example 11 includes the subject matter of any of Examples 1-10, andwherein to convert the representation of speech data comprises toconvert the representation of speech data using a language modelgenerated as a function of a domain-biased web corpus.

Example 12 includes the subject matter of any of Examples 1-11, andwherein to generate the lattice comprises to generate a candidatealternative corresponding to a user intent and associated slots of amapping of the semantic model.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to generate the candidate alternative comprises to generate acandidate alternative matching a contextual semantic feature of thesemantic model.

Example 14 includes the subject matter of any of Examples 1-13, andwherein to generate the candidate alternative comprises to generate acandidate alternative matching an ontological index of the semanticmodel.

Example 15 includes the subject matter of any of Examples 1-14, andwherein to generate the lattice comprises to generate a candidatealternative using a language model, as a function of phonetic similarityto the natural language request.

Example 16 includes the subject matter of any of Examples 1-15, andwherein to generate the lattice comprises to generate a candidatealternative using a semantic enrichment, a contextual feature sequence,an entity-based feature, a relation-based feature, or a non-semanticfiller.

Example 17 includes the subject matter of any of Examples 1-16, andwherein to assign the composite confidence weight further comprises toassign a confidence weight as a function of a language model.

Example 18 includes the subject matter of any of Examples 1-17, andwherein to assign the confidence weight as a function of the languagemodel comprises to assign a phonetic similarity score, a generallanguage model confidence score, a domain language model score, or asyntactic confidence score.

Example 19 includes the subject matter of any of Examples 1-18, andwherein to assign the confidence weight as a function of the semanticmodel comprises to assign an intent-and-slot confidence score.

Example 20 includes the subject matter of any of Examples 1-19, andwherein the request decoder module is further to determine whether atotal confidence weight of the optimal route has a predefinedrelationship to a predefined threshold confidence; and generateadditional candidate alternatives in the lattice of candidatealternatives in response to a determination that the total confidenceweight has the predefined relationship to the predefined thresholdconfidence.

Example 21 includes the subject matter of any of Examples 1-20, andfurther including a dialog management module to process the semanticrepresentation of the natural language request to perform a user dialoginteraction.

Example 22 includes the subject matter of any of Examples 1-21, andfurther including a dialog management module to determine whether thesemantic representation of the natural language request includessufficient information to perform a user intent of the semanticrepresentation; perform the user intent in response to a determinationthat the semantic representation includes sufficient information;generate a response as a function of the semantic representation;generate a natural language representation of the response using thesemantic model; and record a user dialog session including the naturallanguage request and the natural language representation of the responseinto a corpus of recorded user dialog sessions.

Example 23 includes the subject matter of any of Examples 1-22, andwherein to determine whether the semantic representation includessufficient information comprises to determine, using the semantic model,whether the semantic representation includes a mandatory slot associatedwith the user intent of the semantic representation; and to generate theresponse comprises to generate a request for additional informationrelevant to the mandatory slot in response to a determination that thesemantic representation does not include the mandatory slot.

Example 24 includes the subject matter of any of Examples 1-23, andwherein to generate the response comprises to generate a plurality ofpossible responses as a function of the semantic representation; andlimit the response to adequate responses of the plurality of responses,wherein the adequate responses satisfy a relevancy limitation.

Example 25 includes the subject matter of any of Examples 1-24, andwherein to generating the response comprises to determine an additionaluser intent as a function of the user intent of the semanticrepresentation and the corpus of recorded user dialog sessions; andgenerate a response including the additional user intent.

Example 26 includes the subject matter of any of Examples 1-25, andfurther including a tuning module to update the semantic model inresponse to generating the semantic representation.

Example 27 includes the subject matter of any of Examples 1-26, andwherein to update the semantic model comprises to determine the semanticrepresentation was generated with no ambiguities.

Example 28 includes the subject matter of any of Examples 1-27, andwherein to update the semantic model comprises to identify a token ofthe natural language request that was not decoded.

Example 29 includes the subject matter of any of Examples 1-28, andwherein to updating the semantic model comprises to identify anambiguous decoding of a slot of the semantic representation.

Example 30 includes a method for interpreting natural language requests,the method comprising generating, by a computing device, a semanticmodel as a function of a corpus of predefined requests, wherein thesemantic model includes a plurality of mappings between a naturallanguage request and a semantic representation of the natural languagerequest, wherein the semantic representation identifies a user intentand zero or more slots associated with the user intent; generating, bythe computing device using the semantic model, a lattice of candidatealternatives indicative of a natural language request, wherein eachcandidate alternative corresponds to a token of the natural languagerequest; assigning, by the computing device, a composite confidenceweight to each candidate alternative as a function of the semanticmodel; determining, by the computing device, an optimal route throughthe candidate alternative lattice based on the associated confidenceweight; and generating, by the computing device, a semanticrepresentation of the natural language request as a function of thecandidate alternatives of the optimal route.

Example 31 includes the subject matter of Example 30, and whereingenerating the semantic model comprises identifying a contextualsemantic feature in the corpus, wherein the contextual semantic featurecomprises a sequence of lexical sets associated with a user intent andzero or more slots associated with the user intent; determining a firstprobability of the contextual semantic feature given the user intent;and determining a normalized probability of the user intent as afunction of a rate of occurrence of the contextual semantic feature inthe corpus.

Example 32 includes the subject matter of any of Examples 30 and 31, andwherein identifying the contextual semantic feature comprisesidentifying the contextual semantic feature using a semi-supervisedalgorithm.

Example 33 includes the subject matter of any of Examples 30-32, andwherein identifying the contextual semantic feature using thesemi-supervised algorithm comprises tagging a first group of predefinedsample queries in the corpus to identify user intents and slots;extracting the contextual semantic feature from the sample queries;analyzing, using the semantic model, a second group of predefined samplequeries in the corpus; extracting additional contextual semanticfeatures in response to analyzing the second group of predefined samplequeries; and updating the first probability and the normalizedprobability in response to analyzing the second group of predefinedsample queries.

Example 34 includes the subject matter of any of Examples 30-33, andwherein the second group of predefined sample queries comprises recordeduser data or a web corpus.

Example 35 includes the subject matter of any of Examples 30-34, andwherein identifying the contextual semantic feature comprisesidentifying the contextual semantic feature using an unsupervisedalgorithm.

Example 36 includes the subject matter of any of Examples 30-35, andwherein identifying the contextual semantic feature using theunsupervised algorithm comprises identifying predefined named entitiesand relationships in a first group of predefined sample queries in thecorpus; clustering the predefined sample queries using an unsupervisedclustering algorithm to generate a plurality of clusters; and assigninga user intent and slots to each cluster of the plurality of clusters.

Example 37 includes the subject matter of any of Examples 30-36, andwherein generating the semantic model comprises generating anontological index as a function of a predefined ontology associated withthe user intent, wherein the ontology includes a plurality of objectsdescribing a knowledge domain.

Example 38 includes the subject matter of any of Examples 30-37, andwherein generating the ontological index comprises assigning a vectorspace to an object type of the predefined ontology, wherein the vectorspace includes a plurality of coordinates, wherein each coordinateencodes a lexical token representing an associated object of theontology; and mapping a slot of the user intent associated with thepredefined ontology to the vector space.

Example 39 includes the subject matter of any of Examples 30-38, andfurther including receiving, by the computing device, a representationof speech data indicative of the natural language request; andconverting, by the computing device, the representation of speech datato a first lattice of candidate alternatives indicative of the naturallanguage request; wherein generating the lattice of candidatealternatives comprises generating the lattice of candidate alternativesin response to converting the representation of speech data to the firstlattice of candidate alternatives.

Example 40 includes the subject matter of any of Examples 30-39, andwherein converting the representation of speech data comprisesconverting the representation of speech data using a language modelgenerated as a function of a domain-biased web corpus.

Example 41 includes the subject matter of any of Examples 30-40, andwherein generating the lattice comprises generating a candidatealternative corresponding to a user intent and associated slots of amapping of the semantic model.

Example 42 includes the subject matter of any of Examples 30-41, andwherein generating the candidate alternative comprises generating acandidate alternative matching a contextual semantic feature of thesemantic model.

Example 43 includes the subject matter of any of Examples 30-42, andwherein generating the candidate alternative comprises generating acandidate alternative matching an ontological index of the semanticmodel.

Example 44 includes the subject matter of any of Examples 30-43, andwherein generating the lattice comprises generating a candidatealternative using a language model, as a function of phonetic similarityto the natural language request.

Example 45 includes the subject matter of any of Examples 30-44, andwherein generating the lattice comprises generating a candidatealternative using a semantic enrichment, a contextual feature sequence,an entity-based feature, a relation-based feature, or a non-semanticfiller.

Example 46 includes the subject matter of any of Examples 30-45, andwherein assigning the composite confidence weight further comprisesassigning a confidence weight as a function of a language model.

Example 47 includes the subject matter of any of Examples 30-46, andwherein assigning the confidence weight as a function of the languagemodel comprises assigning a phonetic similarity score, a generallanguage model confidence score, a domain language model score, or asyntactic confidence score.

Example 48 includes the subject matter of any of Examples 30-47, andwherein assigning the confidence weight as a function of the semanticmodel comprises assigning an intent-and-slot confidence score.

Example 49 includes the subject matter of any of Examples 30-48, andfurther including determining, by the computing device, whether a totalconfidence weight of the optimal route has a predefined relationship toa predefined threshold confidence; and generating, by the computingdevice, additional candidate alternatives in the lattice of candidatealternatives in response to determining the total confidence weight hasthe predefined relationship to the predefined threshold confidence.

Example 50 includes the subject matter of any of Examples 30-49, andfurther including processing, by the computing device, the semanticrepresentation of the natural language request to perform a user dialoginteraction.

Example 51 includes the subject matter of any of Examples 30-50, andfurther including determining, by the computing device, whether thesemantic representation of the natural language request includessufficient information to perform a user intent of the semanticrepresentation; performing, by the computing device, the user intent inresponse to determining that the semantic representation includessufficient information; generating, by the computing device, a responseas a function of the semantic representation; generating, by thecomputing device, a natural language representation of the responseusing the semantic model; and recording, by the computing device, a userdialog session including the natural language request and the naturallanguage representation of the response into a corpus of recorded userdialog sessions.

Example 52 includes the subject matter of any of Examples 30-51, andwherein determining whether the semantic representation includessufficient information comprises determining, using the semantic model,whether the semantic representation includes a mandatory slot associatedwith the user intent of the semantic representation; and generating theresponse comprises generating a request for additional informationrelevant to the mandatory slot in response to determining that thesemantic representation does not include the mandatory slot.

Example 53 includes the subject matter of any of Examples 30-52, andwherein generating the response comprises generating a plurality ofpossible responses as a function of the semantic representation; andlimiting the response to adequate responses of the plurality ofresponses, wherein the adequate responses satisfy a relevancylimitation.

Example 54 includes the subject matter of any of Examples 30-53, andwherein generating the response comprises determining an additional userintent as a function of the user intent of the semantic representationand the corpus of recorded user dialog sessions; and generating aresponse including the additional user intent.

Example 55 includes the subject matter of any of Examples 30-54, andfurther including updating, by the computing device, the semantic modelin response to generating the semantic representation.

Example 56 includes the subject matter of any of Examples 30-55, andwherein updating the semantic model comprises determining the semanticrepresentation was generated with no ambiguities.

Example 57 includes the subject matter of any of Examples 30-56, andwherein updating the semantic model comprises identifying a token of thenatural language request that was not decoded.

Example 58 includes the subject matter of any of Examples 30-57, andwherein updating the semantic model comprises identifying an ambiguousdecoding of a slot of the semantic representation.

Example 59 includes a computing device comprising a processor; and amemory having stored therein a plurality of instructions that whenexecuted by the processor cause the computing device to perform themethod of any of Examples 30-58.

Example 60 includes one or more machine readable storage mediacomprising a plurality of instructions stored thereon that in responseto being executed result in a computing device performing the method ofany of Examples 30-58.

Example 61 includes a computing device comprising means for performingthe method of any of Examples 30-58.

Example 62 includes a computing device for interpreting natural languagerequests, the computing device comprising means for generating asemantic model as a function of a corpus of predefined requests, whereinthe semantic model includes a plurality of mappings between a naturallanguage request and a semantic representation of the natural languagerequest, wherein the semantic representation identifies a user intentand zero or more slots associated with the user intent; means forgenerating, using the semantic model, a lattice of candidatealternatives indicative of a natural language request, wherein eachcandidate alternative corresponds to a token of the natural languagerequest; means for assigning a composite confidence weight to eachcandidate alternative as a function of the semantic model; means fordetermining an optimal route through the candidate alternative latticebased on the associated confidence weight; and means for generating asemantic representation of the natural language request as a function ofthe candidate alternatives of the optimal route.

Example 63 includes the subject matter of Example 62, and wherein themeans for generating the semantic model comprises means for identifyinga contextual semantic feature in the corpus, wherein the contextualsemantic feature comprises a sequence of lexical sets associated with auser intent and zero or more slots associated with the user intent;means for determining a first probability of the contextual semanticfeature given the user intent; and means for determining a normalizedprobability of the user intent as a function of a rate of occurrence ofthe contextual semantic feature in the corpus.

Example 64 includes the subject matter of any of Examples 62 and 63, andwherein the means for identifying the contextual semantic featurecomprises means for identifying the contextual semantic feature using asemi-supervised algorithm.

Example 65 includes the subject matter of any of Examples 62-64, andwherein the means for identifying the contextual semantic feature usingthe semi-supervised algorithm comprises means for tagging a first groupof predefined sample queries in the corpus to identify user intents andslots; means for extracting the contextual semantic feature from thesample queries; means for analyzing, using the semantic model, a secondgroup of predefined sample queries in the corpus; means for extractingadditional contextual semantic features in response to analyzing thesecond group of predefined sample queries; and means for updating thefirst probability and the normalized probability in response toanalyzing the second group of predefined sample queries.

Example 66 includes the subject matter of any of Examples 62-65, andwherein the second group of predefined sample queries comprises recordeduser data or a web corpus.

Example 67 includes the subject matter of any of Examples 62-66, andwherein the means for identifying the contextual semantic featurecomprises means for identifying the contextual semantic feature using anunsupervised algorithm.

Example 68 includes the subject matter of any of Examples 62-67, andwherein the means for identifying the contextual semantic feature usingthe unsupervised algorithm comprises means for identifying predefinednamed entities and relationships in a first group of predefined samplequeries in the corpus; means for clustering the predefined samplequeries using an unsupervised clustering algorithm to generate aplurality of clusters; and means for assigning a user intent and slotsto each cluster of the plurality of clusters.

Example 69 includes the subject matter of any of Examples 62-68, andwherein the means for generating the semantic model comprises means forgenerating an ontological index as a function of a predefined ontologyassociated with the user intent, wherein the ontology includes aplurality of objects describing a knowledge domain.

Example 70 includes the subject matter of any of Examples 62-69, andwherein the means for generating the ontological index comprises meansfor assigning a vector space to an object type of the predefinedontology, wherein the vector space includes a plurality of coordinates,wherein each coordinate encodes a lexical token representing anassociated object of the ontology; and means for mapping a slot of theuser intent associated with the predefined ontology to the vector space.

Example 71 includes the subject matter of any of Examples 62-70, andfurther including means for receiving a representation of speech dataindicative of the natural language request; and means for converting therepresentation of speech data to a first lattice of candidatealternatives indicative of the natural language request; wherein themeans for generating the lattice of candidate alternatives comprisesmeans for generating the lattice of candidate alternatives in responseto converting the representation of speech data to the first lattice ofcandidate alternatives.

Example 72 includes the subject matter of any of Examples 62-71, andwherein the means for converting the representation of speech datacomprises means for converting the representation of speech data using alanguage model generated as a function of a domain-biased web corpus.

Example 73 includes the subject matter of any of Examples 62-72, andwherein the means for generating the lattice comprises means forgenerating a candidate alternative corresponding to a user intent andassociated slots of a mapping of the semantic model.

Example 74 includes the subject matter of any of Examples 62-73, andwherein the means for generating the candidate alternative comprisesmeans for generating a candidate alternative matching a contextualsemantic feature of the semantic model.

Example 75 includes the subject matter of any of Examples 62-74, and,wherein the means for generating the candidate alternative comprisesmeans for generating a candidate alternative matching an ontologicalindex of the semantic model.

Example 76 includes the subject matter of any of Examples 62-75, andwherein the means for generating the lattice comprises means forgenerating a candidate alternative using a language model, as a functionof phonetic similarity to the natural language request.

Example 77 includes the subject matter of any of Examples 62-76, and,wherein the means for generating the lattice comprises means forgenerating a candidate alternative using a semantic enrichment, acontextual feature sequence, an entity-based feature, a relation-basedfeature, or a non-semantic filler.

Example 78 includes the subject matter of any of Examples 62-77, andwherein the means for assigning the composite confidence weight furthercomprises means for assigning a confidence weight as a function of alanguage model.

Example 79 includes the subject matter of any of Examples 62-78, andwherein the means for assigning the confidence weight as a function ofthe language model comprises means for assigning a phonetic similarityscore, a general language model confidence score, a domain languagemodel score, or a syntactic confidence score.

Example 80 includes the subject matter of any of Examples 62-79, andwherein the means for assigning the confidence weight as a function ofthe semantic model comprises means for assigning an intent-and-slotconfidence score.

Example 81 includes the subject matter of any of Examples 62-80, andfurther including means for determining whether a total confidenceweight of the optimal route has a predefined relationship to apredefined threshold confidence; and means for generating additionalcandidate alternatives in the lattice of candidate alternatives inresponse to determining the total confidence weight has the predefinedrelationship to the predefined threshold confidence.

Example 82 includes the subject matter of any of Examples 62-81, andfurther including means for processing the semantic representation ofthe natural language request to perform a user dialog interaction.

Example 83 includes the subject matter of any of Examples 62-82, andfurther including means for determining whether the semanticrepresentation of the natural language request includes sufficientinformation to perform a user intent of the semantic representation;means for performing the user intent in response to determining that thesemantic representation includes sufficient information; means forgenerating a response as a function of the semantic representation;means for generating a natural language representation of the responseusing the semantic model; and means for recording a user dialog sessionincluding the natural language request and the natural languagerepresentation of the response into a corpus of recorded user dialogsessions.

Example 84 includes the subject matter of any of Examples 62-83, andwherein the means for determining whether the semantic representationincludes sufficient information comprises means for determining, usingthe semantic model, whether the semantic representation includes amandatory slot associated with the user intent of the semanticrepresentation; and the means for generating the response comprisesmeans for generating a request for additional information relevant tothe mandatory slot in response to determining that the semanticrepresentation does not include the mandatory slot.

Example 85 includes the subject matter of any of Examples 62-84, andwherein the means for generating the response comprises means forgenerating a plurality of possible responses as a function of thesemantic representation; and means for limiting the response to adequateresponses of the plurality of responses, wherein the adequate responsessatisfy a relevancy limitation.

Example 86 includes the subject matter of any of Examples 62-85, andwherein the means for generating the response comprises means fordetermining an additional user intent as a function of the user intentof the semantic representation and the corpus of recorded user dialogsessions; and means for generating a response including the additionaluser intent.

Example 87 includes the subject matter of any of Examples 62-86, andfurther including means for updating the semantic model in response togenerating the semantic representation.

Example 88 includes the subject matter of any of Examples 62-87, andwherein the means for updating the semantic model comprises means fordetermining the semantic representation was generated with noambiguities.

Example 89 includes the subject matter of any of Examples 62-88, andwherein the means for updating the semantic model comprises means foridentifying a token of the natural language request that was notdecoded.

Example 90 includes the subject matter of any of Examples 62-89, andwherein the means for updating the semantic model comprises means foridentifying an ambiguous decoding of a slot of the semanticrepresentation.

1. A computing device for interpreting natural language requests, thecomputing device comprising: a semantic compiler module to generate asemantic model as a function of a corpus of predefined requests, whereinthe semantic model includes a plurality of mappings between a naturallanguage request and a semantic representation of the natural languagerequest, wherein the semantic representation identifies a user intentand zero or more slots associated with the user intent; and a requestdecoder module to: generate, using the semantic model, a lattice ofcandidate alternatives indicative of a natural language request, whereineach candidate alternative corresponds to a token of the naturallanguage request; assign a composite confidence weight to each candidatealternative as a function of the semantic model; determine an optimalroute through the candidate alternative lattice based on the associatedconfidence weight; and generate a semantic representation of the naturallanguage request as a function of the candidate alternatives of theoptimal route.
 2. The computing device of claim 1, wherein to generatethe semantic model comprises to: identify a contextual semantic featurein the corpus, wherein the contextual semantic feature comprises asequence of lexical sets associated with a user intent and zero or moreslots associated with the user intent; determine a first probability ofthe contextual semantic feature given the user intent; and determine anormalized probability of the user intent as a function of a rate ofoccurrence of the contextual semantic feature in the corpus.
 3. Thecomputing device of claim 2, wherein to identify the contextual semanticfeature comprises to identify the contextual semantic feature using asemi-supervised algorithm, wherein to identify the contextual semanticfeature using the semi-supervised algorithm comprises to: tag a firstgroup of predefined sample queries in the corpus to identify userintents and slots; extract the contextual semantic feature from thesample queries; analyze, using the semantic model, a second group ofpredefined sample queries in the corpus; extract additional contextualsemantic features in response to analyzing the second group ofpredefined sample queries; and update the first probability and thenormalized probability in response to analyzing the second group ofpredefined sample queries.
 4. The computing device of claim 2, whereinto identify the contextual semantic feature comprises to identify thecontextual semantic feature using an unsupervised algorithm, wherein toidentify the contextual semantic feature using the unsupervisedalgorithm comprises to: identify predefined named entities andrelationships in a first group of predefined sample queries in thecorpus; cluster the predefined sample queries using an unsupervisedclustering algorithm to generate a plurality of clusters; and assign auser intent and slots to each cluster of the plurality of clusters. 5.The computing device of claim 1, wherein to generate the semantic modelcomprises to: generate an ontological index as a function of apredefined ontology associated with the user intent, wherein theontology includes a plurality of objects describing a knowledge domain.6. The computing device of claim 5, wherein to generate the ontologicalindex comprises to: assign a vector space to an object type of thepredefined ontology, wherein the vector space includes a plurality ofcoordinates, wherein each coordinate encodes a lexical tokenrepresenting an associated object of the ontology; and map a slot of theuser intent associated with the predefined ontology to the vector space.7. The computing device of claim 1, wherein the request decoder moduleis further to: receive a representation of speech data indicative of thenatural language request; and convert the representation of speech datato a first lattice of candidate alternatives indicative of the naturallanguage request, wherein to convert the representation of speech datacomprises to convert the representation of speech data using a languagemodel generated as a function of a domain-biased web corpus; wherein togenerate the lattice of candidate alternatives comprises to generate thelattice of candidate alternatives in response to conversion of therepresentation of speech data to the first lattice of candidatealternatives.
 8. The computing device of claim 1, wherein to generatethe lattice comprises to: generate a candidate alternative correspondingto a user intent and associated slots of a mapping of the semanticmodel; and generate a candidate alternative using a language model, as afunction of phonetic similarity to the natural language request.
 9. Thecomputing device of claim 1, wherein to assign the composite confidenceweight further comprises to assign a confidence weight as a function ofa language model.
 10. The computing device of claim 9, wherein to assignthe confidence weight as a function of the language model comprises toassign a phonetic similarity score, a general language model confidencescore, a domain language model score, or a syntactic confidence score.11. The computing device of claim 1, further comprising a dialogmanagement module to: determine whether the semantic representation ofthe natural language request includes sufficient information to performa user intent of the semantic representation; perform the user intent inresponse to a determination that the semantic representation includessufficient information; generate a response as a function of thesemantic representation; generate a natural language representation ofthe response using the semantic model; and record a user dialog sessionincluding the natural language request and the natural languagerepresentation of the response into a corpus of recorded user dialogsessions.
 12. The computing device of claim 1, further comprising atuning module to update the semantic model in response to generating thesemantic representation.
 13. The computing device of claim 12, whereinto update the semantic model comprises to: determine the semanticrepresentation was generated with no ambiguities; identify a token ofthe natural language request that was not decoded; or identify anambiguous decoding of a slot of the semantic representation.
 14. Amethod for interpreting natural language requests, the methodcomprising: generating, by a computing device, a semantic model as afunction of a corpus of predefined requests, wherein the semantic modelincludes a plurality of mappings between a natural language request anda semantic representation of the natural language request, wherein thesemantic representation identifies a user intent and zero or more slotsassociated with the user intent; generating, by the computing deviceusing the semantic model, a lattice of candidate alternatives indicativeof a natural language request, wherein each candidate alternativecorresponds to a token of the natural language request; assigning, bythe computing device, a composite confidence weight to each candidatealternative as a function of the semantic model; determining, by thecomputing device, an optimal route through the candidate alternativelattice based on the associated confidence weight; and generating, bythe computing device, a semantic representation of the natural languagerequest as a function of the candidate alternatives of the optimalroute.
 15. The method of claim 14, wherein generating the semantic modelcomprises: identifying a contextual semantic feature in the corpus,wherein the contextual semantic feature comprises a sequence of lexicalsets associated with a user intent and zero or more slots associatedwith the user intent; determining a first probability of the contextualsemantic feature given the user intent; and determining a normalizedprobability of the user intent as a function of a rate of occurrence ofthe contextual semantic feature in the corpus.
 16. The method of claim14, wherein generating the semantic model comprises: generating anontological index as a function of a predefined ontology associated withthe user intent, wherein the ontology includes a plurality of objectsdescribing a knowledge domain.
 17. The method of claim 14, whereingenerating the lattice comprises: generating a candidate alternativecorresponding to a user intent and associated slots of a mapping of thesemantic model; and generating a candidate alternative using a languagemodel, as a function of phonetic similarity to the natural languagerequest.
 18. The method of claim 14, further comprising: determining, bythe computing device, whether the semantic representation of the naturallanguage request includes sufficient information to perform a userintent of the semantic representation; performing, by the computingdevice, the user intent in response to determining that the semanticrepresentation includes sufficient information; generating, by thecomputing device, a response as a function of the semanticrepresentation; generating, by the computing device, a natural languagerepresentation of the response using the semantic model; and recording,by the computing device, a user dialog session including the naturallanguage request and the natural language representation of the responseinto a corpus of recorded user dialog sessions.
 19. The method of claim14, further comprising: updating, by the computing device, the semanticmodel in response to generating the semantic representation.
 20. One ormore computer-readable storage media comprising a plurality ofinstructions that in response to being executed cause a computing deviceto: generate a semantic model as a function of a corpus of predefinedrequests, wherein the semantic model includes a plurality of mappingsbetween a natural language request and a semantic representation of thenatural language request, wherein the semantic representation identifiesa user intent and zero or more slots associated with the user intent;generate, using the semantic model, a lattice of candidate alternativesindicative of a natural language request, wherein each candidatealternative corresponds to a token of the natural language request;assign a composite confidence weight to each candidate alternative as afunction of the semantic model; determine an optimal route through thecandidate alternative lattice based on the associated confidence weight;and generate a semantic representation of the natural language requestas a function of the candidate alternatives of the optimal route.